Translating in vitro to in vivo and animal to human

Safety and efficacy of toll-like receptor agonists as therapeutic agents and vaccine adjuvants for infectious diseases in animals: a systematic review

Introduction

Strengthening global health security relies on adequate protection against infectious diseases through vaccination and treatment. Toll-like receptor (TLR) agonists exhibit properties that can enhance immune responses, making them potential therapeutic agents or vaccine adjuvants.

Methods

We conducted an extensive systematic review to assess the efficacy of TLR agonists as therapeutic agents or vaccine adjuvants for infectious diseases and their safety profile in animals, excluding rodents and cold-blooded animals. We collected qualitative and available quantitative data on the efficacy and safety outcomes of TLR agonists and employed descriptive analysis to summarize the outcomes.

Results

Among 653 screened studies, 51 met the inclusion criteria. In this review, 82% (42/51) of the studies used TLR agonists as adjuvants, while 18% (9/51) applied TLR agonist as therapeutic agents. The predominant TLR agonists utilized in animals against infectious diseases was CpG ODN, acting as a TLR9 agonist in mammals, and TLR21 agonists in chickens. In 90% (46/51) of the studies, TLR agonists were found effective in stimulating specific and robust humoral and cellular immune responses, thereby enhancing the efficacy of vaccines or therapeutics against infectious diseases in animals. Safety outcomes were assessed in 8% (4/51) of the studies, with one reporting adverse effects.

Discussion

Although TLR agonists are efficacious in enhancing immune responses and the protective efficacy of vaccines or therapeutic agents against infectious diseases in animals, a thorough evaluation of their safety is imperative to in-form future clinical applications in animal studies.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=323122.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Modulation of Angiotensin II-Induced Cellular Hypertrophy by Cannflavin-C: Unveiling the Impact on Cytochrome P450 1B1 and Arachidonic Acid Metabolites

This research aimed to clarify the impacts of cannflavin-C on angiotensin II (Ang II)-induced cardiac hypertrophy and their potential role in modulating cytochrome P450 1B1 (CYP1B1) and arachidonic acid (AA) metabolites. Currently there is no evidence to suggest that cannflavin-C, a prenylated flavonoid, has any significant effects on the heart or cardiac hypertrophy. The metabolism of arachidonic acid (AA) into midchain hydroxyeicosatetraenoic acids (HETEs), facilitated by CYP1B1 enzyme, plays a role in the development of cardiac hypertrophy, which is marked by enlarged cardiac cells. Adult human ventricular cardiomyocyte (AC16) cell line was cultured and exposed to cannflavin-C in the presence and absence of Ang II. The assessment of mRNA expression pertaining to cardiac hypertrophic markers and cytochromes P450 (P450s) was conducted via real-time polymerase chain reaction (PCR), whereas the quantification of P450 protein levels was carried out through western blot analysis. Ang II induced hypertrophic markers myosin heavy chain (β/α-MHC), atrial natriuretic peptide (ANP), and brain natriuretic peptide (BNP) and increased cell surface area, whereas cannflavin-C mitigated these effects. Gene and protein expression analysis revealed that cannflavin-C downregulated CYP1B1 gene expression, protein level, and enzyme activity assessed by 7-methoxyresorufin O-deethylase (MROD). Arachidonic acid metabolites analysis, using liquid chromatography-tandem mass spectrometry (LC-MS/MS), demonstrated that Ang II increased midchain (R/S)-HETE concentrations, which were attenuated by cannflavin-C. This study provides novel insights into the potential of cannflavin-C in modulating arachidonic acid metabolites and attenuating Ang II-induced cardiac hypertrophy, highlighting the importance of this compound as potential therapeutic agents for cardiac hypertrophy. SIGNIFICANCE STATEMENT: This study demonstrates that cannflavin-C offers protection against cellular hypertrophy induced by angiotensin II. The significance of this research lies in its novel discovery, which elucidates a mechanistic pathway involving the inhibition of CYP1B1 by cannflavin-C. This discovery opens up new avenues for leveraging this compound in the treatment of heart failure.

Roadblocks confronting widespread dissemination and deployment of Organs on Chips

Organ on Chip platforms hold significant promise as alternatives to animal models or traditional cell cultures, both of which poorly recapitulate human pathophysiology and human level responses. Within the last 15 years, we have witnessed seminal scientific developments from academic laboratories, a flurry of startups and investments, and a genuine interest from pharmaceutical industry as well as regulatory authorities to translate these platforms. This Perspective identifies several fundamental design and process features that may act as roadblocks that prevent widespread dissemination and deployment of these systems, and provides a roadmap to help position this technology in mainstream drug discovery.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.

The health risks of marine biotoxins associated with high seafood consumption: Looking beyond the single dose, single outcome paradigm with a view towards addressing the needs of coastal Indigenous populations in British Columbia

People who consume high quantities of seafood are at a heightened risk for marine biotoxin exposure. Coastal Indigenous peoples may experience higher levels of risk than the general population due to their reliance on traditional marine foods. Most evidence on the health risks associated with biotoxins focus on a single exposure at one point in time. There is limited research on other types of exposures that may occur among those who regularly consume large quantities of seafood. The objective of this review is to assess what is known about the unique biotoxin exposure risks associated with the consumption patterns of many coastal Indigenous populations. These risks include [1]: repeated exposure to low doses of a single or multiple biotoxins [2]; repeated exposures to high doses of a single or multiple biotoxins; and [3] exposure to multiple biotoxins at a single point in time. We performed a literature search and collected 23 recent review articles on the human health effects of different biotoxins. Using a narrative framework synthesis approach, we collated what is known about the health effects of the exposure risks associated with the putative consumption patterns of coastal Indigenous populations. We found that the health effects of repeated low- or high-dose exposures and the chronic health effects of marine biotoxins are rarely studied or documented. There are gaps in our understanding of how risks differ by seafood species and preparation, cooking, and consumption practices. Together, these gaps contribute to a relatively poor understanding of how biotoxins impact the health of those who regularly consume large quantities of seafood. In the context of this uncertainty, we explore how known and potential risks associated with biotoxins can be mitigated, with special attention to coastal Indigenous populations routinely consuming seafood. Overall, we conclude that there is a need to move beyond the single-dose single-outcome model of exposure to better serve Indigenous communities and others who consume high quantities of seafood.

Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives

Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development

A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Protective Effects of a Strawberry Ellagitannin-Rich Extract against Pro-Oxidative and Pro-Inflammatory Dysfunctions Induced by a High-Fat Diet in a Rat Model

Due to the demonstrated intestinal microbial transformation of strawberry ellagitannins (ET) into bioactive metabolites, in the current study on rats, we hypothesised that the dietary addition of a strawberry ET-rich extract (S-ET) to a high-fat diet (HFD) would attenuate disturbances in the redox and lipid status as well as in the inflammatory response. We randomly distributed 48 Wistar rats into six groups and used two-way analysis of variance (ANOVA) to assess the effects of two main factors—diet type (standard and high-fat) and ET dosage (without, low, and 3× higher)—applied to rats for 4 weeks. In relation to the hypothesis, irrespective of the dosage, the dietary application of ET resulted in the desired attenuating effects in rats fed a HFD as manifested by decreased body weight gain, relative mass of the epididymal pad, hepatic fat, oxidized glutathione (GSSG), triglycerides (TG), total cholesterol (TC), and thiobarbituric acid-reactive substances (TBARS) concentrations as well as desired modifications in the blood plasma parameters. These beneficial changes were enhanced by the high dietary addition of ET, which was associated with considerably higher concentrations of ET metabolites in the urine and plasma of rats. The results indicated that S-ET could be effectively used for the prevention and treatment of metabolic disturbances associated with obesity, dyslipidaemia, redox status imbalance, and inflammation.

Nanoparticle Toxicology

Nanoparticles from natural and anthropogenic sources are abundant in the environment, thus human exposure to nanoparticles is inevitable. Due to this constant exposure, it is critically important to understand the potential acute and chronic adverse effects that nanoparticles may cause to humans. In this review, we explore and highlight the current state of nanotoxicology research with a focus on mechanistic understanding of nanoparticle toxicity at organ, tissue, cell, and biomolecular levels. We discuss nanotoxicity mechanisms, including generation of reactive oxygen species, nanoparticle disintegration, modulation of cell signaling pathways, protein corona formation, and poly(ethylene glycol)-mediated immunogenicity. We conclude with a perspective on potential approaches to advance current understanding of nanoparticle toxicity. Such improved understanding may lead to mitigation strategies that could enable safe application of nanoparticles in humans. Advances in nanotoxicity research will ultimately inform efforts to establish standardized regulatory frameworks with the goal of fully exploiting the potential of nanotechnology while minimizing harm to humans.