In silico approaches in organ toxicity hazard assessment: Current status and future needs for predicting heart, kidney and lung toxicities

Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes a comprehensive integrated Weight of Evidence (WoE) approach to determine the need for a 2-year rat carcinogenicity study. In the present work, experts from different organizations have joined efforts to standardize as much as possible a procedural framework for the integration of evidence associated with the different ICH S1B(R1) WoE criteria. The framework uses a pragmatic consensus procedure for carcinogenicity hazard assessment to facilitate transparent, consistent, and documented decision-making and it discusses best-practices both for the organization of studies and presentation of data in a format suitable for regulatory review. First, it is acknowledged that the six WoE factors described in the addendum form an integrated network of evidence within a holistic assessment framework that is used synergistically to analyze and explain safety signals. Second, the proposed standardized procedure builds upon different considerations related to the primary sources of evidence, mechanistic analysis, alternative methodologies and novel investigative approaches, metabolites, and reliability of the data and other acquired information. Each of the six WoE factors is described highlighting how they can contribute evidence for the overall WoE assessment. A suggested reporting format to summarize the cross-integration of evidence from the different WoE factors is also presented. This work also notes that even if a 2-year rat study is ultimately required, creating a WoE assessment is valuable in understanding the specific factors and levels of human carcinogenic risk better than have been identified previously with the 2-year rat bioassay alone.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.

Isolation and in silico investigation of cannflavins from Cannabis sativa leaves as potential anti-SARS-CoV-2 agents targeting the Papain-Like Protease

This study aimed to isolate and identify three prenylflavonoids (cannflavin A, B, and C) from Cannabis sativa leaves using different chromatographic techniques. The potential of the isolated compounds against SARS-CoV-2 was suggested through several in silico analysis. Structural similarity studies against nine co-crystallized ligands of SARS-CoV-2's proteins indicated the similarities of the isolated cannflavins with the SARS-CoV-2 Papain-Like Protease (PLP) ligand, Y95. Then, flexible allignment study confirmed this similarity. Docking experiments showed successful binding of all cannflavins within the active pocket of PLP, with energies comparable to Y95. Among them, cannflavin A demonstrated the most similar binding mode, while cannflavin C exhibited the best energy. Molecular dynamics (MD) simulations and MM-GPSA confirmed the accurate binding of cannflavin A to the PLP. In silico ADMET studies indicated favourable drug-like properties for all three compounds, suggesting their potential as anti-SARS-CoV-2 agents. Further In vitro and In vivo investigations are necessary to validate these findings and establish their efficacy and safety profiles.

Currently Used Methods to Evaluate the Efficacy of Therapeutic Drugs and Kidney Safety

Kidney diseases with kidney failure or damage, such as chronic kidney disease (CKD) and acute kidney injury (AKI), are common clinical problems worldwide and have rapidly increased in prevalence, affecting millions of people in recent decades. A series of novel diagnostic or predictive biomarkers have been discovered over the past decade, enhancing the investigation of renal dysfunction in preclinical studies and clinical risk assessment for humans. Since multiple causes lead to renal failure, animal studies have been extensively used to identify specific disease biomarkers for understanding the potential targets and nephropathy events in therapeutic insights into disease progression. Mice are the most commonly used model to investigate the mechanism of human nephropathy, and the current alternative methods, including in vitro and in silico models, can offer quicker, cheaper, and more effective methods to avoid or reduce the unethical procedures of animal usage. This review provides modern approaches, including animal and nonanimal assays, that can be applied to study chronic nonclinical safety. These specific situations could be utilized in nonclinical or clinical drug development to provide information on kidney disease.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor.

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/ .

Antibacterial and Cytotoxic Effects of Biosynthesized Zinc Oxide and Titanium Dioxide Nanoparticles

Nanotechnology is a rapidly developing field of research that studies materials having dimensions of less than 100 nanometers. It is applicable in many areas of life sciences and medicine including skin care and personal hygiene, as these materials are the essential components of various cosmetics and sunscreens. The aim of the present study was to synthesize Zinc oxide (ZnO) and Titanium dioxide (TiO₂) nanoparticles (NPs) by using Calotropis procera (C. procera) leaf extract. Green synthesized NPs were characterized by UV spectroscopy, Fourier transform infrared (FTIR), X-ray diffraction (XRD), and Scanning Electron Microscopy (SEM) to investigate their structure, size, and physical properties. The antibacterial and synergistic effects of ZnO and TiO₂ NPs along with antibiotics were also observed against bacterial isolates. The antioxidant activity of synthesized NPs was analyzed by their α-diphenyl-β-picrylhydrazyl (DPPH) radical scavenging activity. In vivo toxic effects of the synthesized NPs were evaluated in albino mice at different doses (100, 200, and 300 mg/kg body weight) of ZnO and TiO₂ NPs administered orally for 7, 14, and 21 days. The antibacterial results showed that the zone of inhibition (ZOI) was increased in a concentration-dependent manner. Among the bacterial strains, Staphylococcus aureus showed the highest ZOI, i.e., 17 and 14 mm against ZnO and TiO₂ NPs, respectively, while Escherichia coli showed the lowest ZOI, i.e., 12 and 10 mm, respectively. Therefore, ZnO NPs are potent antibacterial agents compared to TiO₂ NPs. Both NPs showed synergistic effects with antibiotics (ciprofloxacin and imipenem). Moreover, the DPPH activity showed that ZnO and TiO₂ NPs have significantly (p > 0.05) higher antioxidant activity, i.e., 53% and 58.7%, respectively, which indicated that TiO₂ has good antioxidant potential compared to ZnO NPs. However, the histological changes after exposure to different doses of ZnO and TiO₂ NPs showed toxicity-related changes in the structure of the kidney compared to the control group. The current study provided valuable information about the antibacterial, antioxidant, and toxicity impacts of green synthesized ZnO and TiO₂ NPs, which can be influential in the further study of their eco-toxicological effects.

Alternative Methods as Tools for Obesity Research: In Vitro and In Silico Approaches

The study of adipogenesis is essential for understanding and treating obesity, a multifactorial problem related to body fat accumulation that leads to several life-threatening diseases, becoming one of the most critical public health problems worldwide. In this review, we propose to provide the highlights of the adipogenesis study based on in vitro differentiation of human mesenchymal stem cells (hMSCs). We list in silico methods, such as molecular docking for identification of molecular targets, and in vitro approaches, from 2D, more straightforward and applied for screening large libraries of substances, to more representative physiological models, such as 3D and bioprinting models. We also describe the development of physiological models based on microfluidic systems applied to investigate adipogenesis in vitro. We intend to identify the main alternative models for adipogenesis evaluation, contributing to the direction of preclinical research in obesity. Future directions indicate the association of in silico and in vitro techniques to bring a clear picture of alternative methods based on adipogenesis as a tool for obesity research.

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods

Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity

Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.