Today's Challenges to De-Risk and Predict Drug Safety in Human "Mind-the-Gap"

Points to consider for revising the ICH S7A guideline on safety and secondary pharmacology

Although the ICH S7A guideline on safety pharmacology largely achieved its objective, a proportion of remaining adverse drug reactions and attrition can be attributed in part to gaps in safety and secondary pharmacology assessments. Advances in science, technology, drug development paradigm and regulatory practices necessitate revisiting and evolving ICH S7A to address these limitations. The anticipated completion of the ICH S7B Q&As by end of 2025 provides an opportunity to integrate its outcomes with ICH S7A into a comprehensive, modality-agnostic sustainable over time framework. Such consolidation could streamline guidance, enhance usability, and align regulatory expectations globally. This proposed revision should aim to address key aspects of safety and secondary pharmacology, including the definition of adversity, the integration of human-relevant in vitro and in silico models, and the adoption of state-of-the-art in vivo platforms. Further considerations should include the development of principles for model and assay validation, the promotion of integrated risk assessment frameworks, and incorporation of weight of evidence approaches. Revised guideline would also emphasize sustainable practices by adapting to evolving therapeutic modalities, while reducing reliance on animal testing through New Approach Methodologies. The revision seeks to enhance the benefit-risk evaluation of drug candidates, refine clinical monitoring, foster regulatory acceptance, and streamline drug development. This comprehensive update has the potential to not only optimize drug safety evaluations but also to align industry practices with modern scientific advancements and ethical considerations, ensuring a more robust and efficient pathway for therapeutic innovation.

Human induced pluripotent stem cell-derived cardiomyocytes and their use in a cardiac organ-on-a-chip to assay electrophysiology, calcium and contractility

Cardiac organs-on-a-chip (OoCs) or microphysiological systems have the potential to predict cardiac effects of new drug candidates, including unanticipated cardiac outcomes, which are among the main causes for drug attrition. This protocol describes how to prepare and use a cardiac OoC containing cardiomyocytes differentiated from human induced pluripotent stem cells (hiPS cells). The use of cells derived from hiPS cells as reliable sources of human cells from diverse genetic backgrounds also holds great potential, especially when cultured in OoCs that are physiologically relevant culture platforms. To promote the broad adoption of hiPS cell-derived cardiac OoCs in the drug development field, there is a need to first ensure reproducibility in their preparation and use. This protocol aims to provide key information on how to reduce sources of variability during hiPS cell maintenance, differentiation, loading and maturation in OoCs. Variability in these procedures can lead to inconsistent purity after differentiation and variable function between batches of microtissues formed in OoCs. This protocol also focuses on describing the handling and functional assessment of cardiac microtissues using live-cell microscopy approaches to quantify parameters of cellular electrophysiology, calcium transients and contractility. The protocol consists of five stages: (1) thaw and maintain hiPS cells, (2) differentiate hiPS cell cardiomyocytes, (3) load differentiated cells into OoCs, (4) maintain and characterize loaded cells, and (5) evaluate and utilize cardiac OoCs. Execution of the entire protocol takes ~40 days. The required skills to carry out the protocol are experience with sterile techniques, mammalian cell culture and maintaining hiPS cells in a pluripotent state.

Ultrasound-Assisted Synthesis of Pyrazoline Derivatives as Potential Antagonists of RAGE-Mediated Pathologies: Insights from SAR Studies and Biological Evaluations

In the context of age-related disorders, the receptor of advanced glycation end products (RAGE), plays a pivotal role in the pathogenesis of these conditions by triggering downstream signaling pathways associated with chronic inflammation and oxidative stress. Targeting this inflammaging phenomenon with RAGE antagonists holds promise for interventions with broad implications in healthy aging and the management of age-related conditions. This study explores the structure-activity relationship (SAR) of pyrazoline-based RAGE antagonists synthesized using an ultrasound-assisted green one-pot two-steps methodology. Our investigation identifies phenylurenyl-pyrazoline 2 g as a promising candidate, demonstrating superior efficiency compared to the reference antagonist Azeliragon (IC50=13 μM). Compound 2 g exhibits potent inhibition of the AGE2-BSA/sRAGE interaction (IC50=22 μM) and favorable affinity in Microscale Thermophoresis (MST) assays (Kd=17.1 μM), along with a favorable safety profile, with no apparent cytotoxicity observed in vitro in the MTS assay. These findings underscore the potential of pyrazoline-derived RAGE antagonists as therapeutic agents for addressing age-related disorders.

Assessing the interplay between off-target promiscuity, cytotoxicity, and tolerability in rodents to improve the safety profile of novel anti-malarial plasmepsin X inhibitors

Within drug development, high off-target promiscuity as well as potent cytotoxicity, are associated with a high attrition rate. We investigated the safety profile of novel plasmepsin X (PMX) inhibitors for the treatment of malaria. In our screening cascade, a total of 249 PMX compounds were profiled in a panel of in vitro secondary pharmacology assays containing 44 targets (SafetyScreen44 panel) and in a cytotoxicity assay in HepG2 cells using ATP as an endpoint. Six of the lead compounds were subsequently tested in a 7-d rat toxicology study, and/or in a cardiovascular study in guinea pigs. Overall, compounds with high cytotoxicity in HepG2 cells correlated with high promiscuity (off-target hit rate >20%) in the SafetyScreen44 panel and were associated with poor tolerability in vivo (decedents, morbidity, adverse clinical signs, or severe cardiovascular effects). Some side effects observed in rats or guinea pigs could putatively be linked with hits in the secondary pharmacological profiling, such as the M1 or M2 muscarinic acetylcholine receptor, opioid µ and/or κ receptors or hERG/CaV1.2/Na+ channels, which were common to >50% the compounds tested in vivo. In summary, compounds showing high cytotoxicity and high promiscuity are likely to be poorly tolerated in vivo. However, such associations do not necessarily imply a causal relationship. Identifying the targets that cause these undesirable effects is key for early safety risk assessment. A tiered approach, based on a set of in vitro assays, helps selecting the compounds with highest likelihood of success to proceed to in vivo toxicology studies.

Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes

Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

INTRODUCTION

Drug induced Liver-Injury (DILI) is a leading cause of drug attrition and complex in vitro models (CIVMs), including three dimensional (3D) spheroids, 3D bio printed tissues and flow-based systems, could improve preclinical prediction. Although CIVMs have demonstrated good sensitivity and specificity in DILI detection their adoption remains limited.

AREAS COVERED

This article describes DILI, the challenges with its prediction and the current strategies and models that are being used. It reviews data from industry-FDA collaborations and strategic partnerships and finishes with an outlook of CIVMs in preclinical toxicity testing. Literature searches were performed using PubMed and Google Scholar while product information was collected from manufacturer websites.

EXPERT OPINION

Liver CIVMs are promising models for predicting DILI although, a decade after their introduction, routine use by the pharmaceutical industry is limited. To accelerate their adoption, several industry-regulator-developer partnerships or consortia have been established to guide the development and qualification. Beyond this, liver CIVMs should continue evolving to capture greater immunological mimicry while partnering with computational approaches to deliver systems that change the paradigm of predicting DILI.

The state of the art in secondary pharmacology and its impact on the safety of new medicines

Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.

Comparison of electrocardiogram and blood pressure recording methods in non-rodent toxicology studies: A retrospective analysis

Our study retrospectively examines 51 non-rodent general toxicology studies conducted over the past 8 years to ascertain the influence of recording methodologies on baseline cardiovascular (CV) parameters and statistical sensitivity. Specifically, our work aims to evaluate the frequency of cardiovascular parameter recording categorized by therapeutic modality and study type, to assess the variability in these parameters based on measurement techniques, and to determine the sample sizes needed for detecting relevant changes in heart rate (HR), blood pressure (BP), and QTc interval in non-human primate (NHP) studies. Results indicate that electrocardiogram (ECG) measurements in dogs and NHP were recorded in 63% of studies, combined with BP recording in 18% of studies, while BP was never recorded alone. Trend analysis reveals a decline in the utilisation of restraint-based methods for ECG measurements post-2017, to the benefit of telemetry-based recordings, particularly Jacketed External Telemetry (JET). There was a marked difference in baseline values, with restraint-based methods showing significantly higher HR and QTc values compared to JET, likely linked to animal stress. Further analysis suggests an unrealistic and unethical sample size requirement in NHP studies for detecting biologically meaningful CV parameter changes using restraint-based methods, while JET methods necessitate significantly smaller sample sizes. This retrospective study indicates a notable shift from snapshots short-duration, restraint-based methods towards telemetry approaches over the recent years, especially with an increased usage of implanted telemetry. The transition contributes to potential consensus within industry or regulatory frameworks for optimal practices in assessing ECG, HR, and BP in general toxicology studies.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

In vitro immunity: an overview of immunocompetent organ-on-chip models

Impressive advances have been made to replicate human physiology in vitro over the last few years due to the growth of the organ-on-chip (OoC) field in both industrial and academic settings. OoCs are a type of microphysiological system (MPS) that imitates functional and dynamic aspects of native human organ biology on a microfluidic device. Organoids and organotypic models, ranging in their complexity from simple single-cell to complex multi-cell type constructs, are being incorporated into OoC microfluidic devices to better mimic human physiology. OoC technology has now progressed to the stage at which it has received official recognition by the Food and Drug Administration (FDA) for use as an alternative to standard procedures in drug development, such as animal studies and traditional in vitro assays. However, an area that is still lagging behind is the incorporation of the immune system, which is a critical element required to investigate human health and disease. In this review, we summarise the progress made to integrate human immunology into various OoC systems, specifically focusing on models related to organ barriers and lymphoid organs. These models utilise microfluidic devices that are either commercially available or custom-made. This review explores the difference between the use of innate and adaptive immune cells and their role for modelling organ-specific diseases in OoCs. Immunocompetent multi-OoC models are also highlighted and the extent to which they recapitulate systemic physiology is discussed. Together, the aim of this review is to describe the current state of immune-OoCs, the limitations and the future perspectives needed to improve the field.

Collaborative science in action: A 20 year perspective from the Health and Environmental Sciences Institute (HESI) Cardiac Safety Committee

The Health and Environmental Sciences Institute (HESI) is a nonprofit organization dedicated to resolving global health challenges through collaborative scientific efforts across academia, regulatory authorities and the private sector. Collaborative science across non-clinical disciplines offers an important keystone to accelerate the development of safer and more effective medicines. HESI works to address complex challenges by leveraging diverse subject-matter expertise across sectors offering access to resources, data and shared knowledge. In 2008, the HESI Cardiac Safety Committee (CSC) was established to improve public health by reducing unanticipated cardiovascular (CV)-related adverse effects from pharmaceuticals or chemicals. The committee continues to significantly impact the field of CV safety by bringing together experts from across sectors to address challenges of detecting and predicting adverse cardiac outcomes. Committee members have collaborated on the organization, management and publication of prospective studies, retrospective analyses, workshops, and symposia resulting in 38 peer reviewed manuscripts. Without this collaboration these manuscripts would not have been published. Through their work, the CSC is actively addressing challenges and opportunities in detecting potential cardiac failure modes using in vivo, in vitro and in silico models, with the aim of facilitating drug development and improving study design. By examining past successes and future prospects of the CSC, this manuscript sheds light on how the consortium's multifaceted approach not only addresses current challenges in detecting potential cardiac failure modes but also paves the way for enhanced drug development and study design methodologies. Further, exploring future opportunities and challenges will focus on improving the translational predictability of nonclinical evaluations and reducing reliance on animal research in CV safety assessments.

Preclinical cardiovascular safety assessment of pharmacology-toxicology relationship for a set of novel kinase inhibitors

Cardiovascular toxicity is one of the more common causes of attrition in preclinical and clinical drug development. Preclinical cardiovascular safety assessment involves numerous in vitro and in vivo endpoints which are being continually reviewed and improved to lower the incidence of cardiovascular toxicity that manifests only after the initiation of clinical trials. An example of notable preclinical toxicity is necrosis in the papillary muscle of the left ventricle in dogs that is induced by exaggerated pharmacological effects of vasodilators or positive inotropic/vasodilating off-target drug effects. Two distinct, small-molecule inhibitors that target an intracellular kinase, Compound A and Compound B, were profiled in 2-week dose-range finding and 4-week toxicity studies. Serum cardiac troponin (cTnI) was evaluated after a single dose and after 2-week and 4-week repeat dose studies with each kinase inhibitor. Acute effects on hemodynamic (heart rate, blood pressures, left ventricular contractility) and electrocardiographic (QTcV, PR, QRS intervals) endpoints by each inhibitor were assessed in an anesthetized dog cardiovascular model. Cardiovascular degeneration/necrosis with and without fibrosis was observed in dogs and correlated to increases in serum cTnI in repeat-dose toxicity studies. At the same doses used in toxicologic assessments, both kinase inhibitors produced sustained increases in heart rate, left ventricular contractility, and cardiac output, and decreases in mean arterial pressure. Cardiac pathology findings associated with these 2 kinase inhibitors were accompanied not only by cardiac troponin elevations but also associated with hemodynamic changes, highlighting the importance of the link of the physiologic-toxicologic interplay in cardiovascular safety assessment.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies, diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of frontotemporal dementia, make up the vast majority of dementia cases. Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments, ongoing progress is required to ensure these are effective, economical, and accessible for the globally ageing population. As such, continued identification of new potential drug targets and biomarkers is critical. "Big data" studies, such as proteomics, can generate information on thousands of possible new targets for dementia diagnostics and therapeutics, but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development. In this review, we discuss current tauopathy biomarkers and therapeutics, and highlight areas in need of improvement, particularly when addressing the needs of frail, comorbid and cognitively impaired populations. We highlight biomarkers which have been developed from proteomic data, and outline possible future directions in this field. We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development, and demonstrate its application to our group's recent tau interactome dataset as an example.

An IQ consortium analysis of starting dose selection for oncology small molecule first-in-patient trials suggests an alternative NOAEL-based method can be safe while reducing time to the recommended phase 2 dose

The first-in-patient (FIP) starting dose for oncology agents should be reasonably safe and provide potential therapeutic benefit to the patient. For late-stage oncology patients, this dose is often based on the ICH S9 guidance, which was developed primarily based on experience with cytotoxic chemotherapeutic agents using the rodent STD10 or non-rodent HNSTD and an appropriate safety factor. With the increase in molecularly targeted chemotherapeutics, it is prudent to re-evaluate how the FIP dose is derived to ensure that the appropriate balance between risk and therapeutic benefit to the patient is achieved. Blinded data on 92 small molecule oncology compounds from 12 pharmaceutical companies who are members of the IQ DruSafe consortium were gathered to investigate if a NOAEL-based starting dose without a safety factor would have been tolerated in the FIP trial and if so, estimating how many dose escalation cohorts could have been reduced. Our analysis suggests that the NOAEL-based alternative starting dose would have been tolerated in most cases evaluated, with an anticipated mean reduction of 2.3 cohorts. Of the 12 cases where the alternative approach resulted in a starting dose that would have exceeded the MTD/RP2D, none of the nonclinical toxicities in these cases were considered irreversible and would be monitorable in all but one instance. Most non-tolerated cases were within two-threefold of the MTD/RP2D, with the clinical AEs considered manageable and mitigated by dose de-escalation. No one method of FIP dose calculation will likely be appropriate for all oncology small molecules and starting dose selection should be performed using a case-by-case approach. However, the NOAEL-based method that does not utilize a safety factor should be considered when appropriate to minimize the number of patients exposed to sub-therapeutic doses of an investigational oncology agent and accelerating development to RP2D.

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

The 3Rs in Experimental Liver Disease

Patients with cirrhosis present multiple physiological and immunological alterations that play a very important role in the development of clinically relevant secondary complications to the disease. Experimentation in animal models is essential to understand the pathogenesis of human diseases and, considering the high prevalence of liver disease worldwide, to understand the pathophysiology of disease progression and the molecular pathways involved, due to the complexity of the liver as an organ and its relationship with the rest of the organism. However, today there is a growing awareness about the sensitivity and suffering of animals, causing opposition to animal research among a minority in society and some scientists, but also about the attention to the welfare of laboratory animals since this has been built into regulations in most nations that conduct animal research. In 1959, Russell and Burch published the book "The Principles of Humane Experimental Technique", proposing that in those experiments where animals were necessary, everything possible should be done to try to replace them with non-sentient alternatives, to reduce to a minimum their number, and to refine experiments that are essential so that they caused the least amount of pain and distress. In this review, a comprehensive summary of the most widely used techniques to replace, reduce, and refine in experimental liver research is offered, to assess the advantages and weaknesses of available experimental liver disease models for researchers who are planning to perform animal studies in the near future.

Evaluation of chronic drug-induced electrophysiological and cytotoxic effects using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs)

Introduction: Cardiotoxicity is one of the leading causes of compound attrition during drug development. Most in vitro screening platforms aim at detecting acute cardio-electrophysiological changes and drug-induced chronic functional alterations are often not studied in the early stage of drug development. Therefore, we developed an assay using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) that evaluates both drug-induced acute and delayed electrophysiological and cytotoxic effects of reference compounds with clinically known cardiac outcomes. Methods: hiPSC-CMs were seeded in 48-well multielectrode array (MEA) plates and were treated with four doses of reference compounds (covering and exceeding clinical free plasma peak concentrations -fC_max values) and MEA recordings were conducted for 4 days. Functional-electrophysiological (field-potentials) and viability (impedance) parameters were recorded with a MEA machine. Results: To assess this platform, we tested tyrosine-kinase inhibitors with high-cardiac risk profile (sunitinib, vandetanib and nilotinib) and low-cardiac risk (erlotinib), as well as known classic cardiac toxic drugs (doxorubicin and BMS-986094), ion-channel trafficking inhibitors (pentamidine, probucol and arsenic trioxide) and compounds without known clinical cardiotoxicity (amoxicillin, cetirizine, captopril and aspirin). By evaluating the effects of these compounds on MEA parameters, the assay was mostly able to recapitulate different drug-induced cardiotoxicities, represented by a prolongation of the field potential, changes in beating rate and presence of arrhythmic events in acute (<2 h) or delayed phase ≥24 h, and/or reduction of impedance during the delayed phase (≥24 h). Furthermore, a few reference compounds were tested in hiPSC-CMs using fluorescence- and luminescence-based plate reader assays, confirming the presence or absence of cytotoxic effects, linked to changes of the impedance parameters measured in the MEA assay. Of note, some cardiotoxic effects could not be identified at acute time points (<2 h) but were clearly detected after 24 h, reinforcing the importance of chronic drug evaluation. Discussion: In conclusion, the evaluation of chronic drug-induced cardiotoxicity using a hiPSC-CMs in vitro assay can contribute to the early de-risking of compounds and help optimize the drug development process.

Investigating the relevance of CYP2J2 inhibition for drugs known to cause intermediate to high risk torsades de pointes

Cardiac cytochrome P450 2J2 (CYP2J2) metabolizes endogenous polyunsaturated fatty acid, arachidonic acid (AA), to bioactive regioisomeric epoxyeicosatrienoic acid (EET) metabolites. This endogenous metabolic pathway has been postulated to play a homeostatic role in cardiac electrophysiology. However, it is unknown if drugs that cause intermediate to high risk torsades de pointes (TdP) exhibit inhibitory effects against CYP2J2 metabolism of AA to EETs. In this study, we demonstrated that 11 out of 16 drugs screened with intermediate to high risk of TdP as defined by the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative are concurrently reversible inhibitors of CYP2J2 metabolism of AA, with unbound inhibitory constant (K_i,AA,u) values ranging widely from 0.132 to 19.9 µM. To understand the physiological relevancy of K_i,AA,u, the in vivo unbound drug concentration within human heart tissue (C_u,heart) was calculated via experimental determination of in vitro unbound partition coefficient (K_puu) for 10 CYP2J2 inhibitors using AC16 human ventricular cardiomyocytes as well as literature-derived values of fraction unbound in plasma (f_u,p) and plasma drug concentrations in clinical scenarios leading to TdP. Notably, all CYP2J2 inhibitors screened belonging to the high TdP risk category, namely vandetanib and bepridil, exhibited highest K_puu values of 18.2 ± 1.39 and 7.48 ± 1.16 respectively although no clear relationship between C_u,heart and risk of TdP could eventually be determined. R values based on basic models of reversible inhibition as per FDA guidelines were calculated using unbound plasma drug concentrations (C_u,plasma) and adapted using C_u,heart which suggested that 4 out of 10 CYP2J2 inhibitors with intermediate to high risk of TdP demonstrate greatest potential for clinically relevant in vivo cardiac drug-AA interactions. Our results shed novel insights on the relevance of CYP2J2 inhibition in drugs with risk of TdP. Further studies ascertaining the role of CYP2J2 metabolism of AA in cardiac electrophysiology, characterizing inherent cardiac ion channel activities of drugs with risk of TdP as well as in vivo evidence of drug-AA interactions will be required prior to determining if CYP2J2 inhibition could be an alternative mechanism contributing to drug-induced TdP.

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.

2000-2023 over two decades of ICH S7A: has the time come for a revamp?

The ICH S7A guideline on safety pharmacology studies released over 20 years ago largely achieved its objective "to help protect clinical trial participants and patients receiving marketed products from potential adverse effects of pharmaceuticals". Although, Phase I clinical trials are generally very safe, the incidence and severity of adverse events, the safety related attrition and product withdrawal remain elevated during late-stage clinical development and post approval, a proportion of which can be attributed at least in part to safety pharmacology related issues. Considering the latest scientific and technological advancements in drug safety science, the paradigm shift of the drug discovery and development process and the continuously evolving regulatory landscape, we recommend revisiting, adapting and evolving the ICH S7A guideline. This might offer opportunities i) to select and progress optimized drugs with increased confidence in success, ii) to refine and adapt the clinical monitoring at all stages of clinical development resulting in an optimized benefit/risk assessment, iii) to increase likelihood of regulatory acceptance in a way compatible with an expedited and streamlined drug discovery and development process to benefit patients and iv) to avoid the unnecessary use of animals in 'tick-the-box' studies and encourage alternative approaches. As presented in the article, several options could be envisioned to revisit and adapt the ICH S7A taking into consideration several key features.

Development of a checklist for the assessment of pharmacovigilance guidelines in Southern Africa: a document review

Introduction

National regulatory systems in Southern Africa reflect various stages of maturity, and pharmacovigilance (PV) practices are not aligned. In the absence of guidance for formulating PV guidelines in Southern African Development Community (SADC) countries, this study aimed to create a checklist that may be used to assess the rigour of PV guidelines in this region and provide guidance for the National Medicines Regulatory Agency (NMRA) authors.

Methods

A document analysis was performed based on harmonised international guidelines (n = 22) that prescribed methods of PV regulation to identify themes and items to incorporate into a checklist. The contextualisation of the checklist to the African pharmaceutical environment was accomplished by referencing peer-reviewed journal articles (n = 7). The checklist was subjected to face and content validation by non-experts and PV experts.

Results

The document review yielded 5 themes, 18 sub-themes, and 73 items structured into the checklist. Themes encompassed PV systems, definitions, individual case safety reporting, aggregate reporting, and risk management. Under PV systems, aspects of the quality management system were outlined, that is, the legal basis for PV, a description of the marketing authorisation holder's (MAH's) PV system, archiving of data, contracting of PV tasks, and the duties of the person responsible for the MAH's PV obligations. Definitions of the key terms and major stakeholders were identified. Reporting of individual case safety reports (ICSRs) was explicated by considering the criteria for reporting, categories of reportable information, expedited reporting requirements, reporting timelines, and ICSR reporting format. Aggregate report submission during the development and post-marketing phases was addressed. Risk management encompassed signal detection, re-evaluation of the benefit-risk ratio, the safety decision-making process, risk management planning, risk minimisation and safety communication.

Conclusion

The developed checklist can contribute towards assisting SADC NMRAs to formulate national PV guidelines that reflect current international practice, with local context incorporated.

Plain Language Summary

Developing a checklist for the evaluation of medicine safety guidelines in Southern Africa Introduction: In Southern African Development Community (SADC) countries, the guidelines for medicine safety [pharmacovigilance (PV)] that marketing authorisation holders (MAHs) and healthcare professionals need to adhere to, are not aligned. We saw the need to develop a checklist that can be used to evaluate these guidelines.Methods: We studied international documents issued by the World Health Organization (WHO), the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), the Council for International Organizations of Medical Sciences (CIOMS) and the European Medicines Agency (EMA). On the organisational websites, we obtained 22 documents and identified 73 checklist items. All the items were arranged under 5 themes and 18 sub-themes to create the checklist. We adapted the checklist to the local context by using seven journal articles addressing PV concerns in Africa. Experts checked the content and usability of the checklist.Results: The themes were PV systems, definitions, individual case safety reporting (ICSR), combined reporting and risk management. PV systems had six sub-themes: legal structure, description of the MAH's PV system, contractual agreements, information storage, the qualified person responsible for PV (QPPV) and where the QPPV is located. We included the definitions of keywords and role-players. The ICSR theme had five sub-themes, i.e. criteria for reporting, categories of reportable information, expedited reporting, reporting timelines, and reporting format. Submission of summary reports comprised an overview of the safety profile of a medicine once it is approved by regulators, as well as during clinical trials. Risk management included signal detection, re-evaluation of the benefit-risk ratio, safety decision-making process, risk management planning, risk minimisation, and safety communication. The checklist is applied by allocating yes/no scoring per item.Conclusion: The checklist may be used by regulators within SADC to assess their PV guidelines for alignment with international standards and suitability to the local environment.

Human-Induced Pluripotent Stem Cell-Based Differentiation of Cardiomyocyte Subtypes for Drug Discovery and Cell Therapy

Drug attrition rates have increased over the past few years, accompanied with growing costs for the pharmaceutical industry and consumers. Lack of in vitro models connecting the results of toxicity screening assays with clinical outcomes accounts for this high attrition rate. The emergence of cardiomyocytes derived from human pluripotent stem cells provides an amenable source of cells for disease modeling, drug discovery, and cardiotoxicity screening. Functionally similar to to embryonic stem cells, but with fewer ethical concerns, induced pluripotent stem cells (iPSCs) can recapitulate patient-specific genetic backgrounds, which would be a huge revolution for personalized medicine. The generated iPSC-derived cardiomyocytes (iPSC-CMs) represent different subtypes including ventricular-, atrial-, and nodal-like cardiomyocytes. Purifying these subtypes for chamber-specific drug screening presents opportunities and challenges. In this chapter, we discuss the strategies for the purification of iPSC-CMs, the use of iPSC-CMs for drug discovery and cardiotoxicity test, and the current limitations of iPSC-CMs that should be overcome for wider and more precise cardiovascular applications.

Challenges and shortcomings of antibacterial discovery projects

OBJECTIVES

Antibacterial drug discovery activities are essential for filling clinical pipelines with promising clinical candidates. Little information is available about the challenges and shortcomings of small companies and academic institutions in performing these important discovery tasks.

METHODS

We performed a content analysis of 463 reviewer comments on 91 funding applications of antibacterial drug discovery projects submitted to two major global funders between 2016 and 2020 that had not proceeded further in the selection process. This quality assessment was complemented with the inputs (via e-mail) from a panel involving six antibiotic research and development (R&D) experts with long-standing expertise and experience in antibiotic drug discovery.

RESULTS

Common critical comments of reviewers are grouped into three main categories: scientific and technical shortcomings, unclear potential societal impact, and insufficient capability and expertise of the project team regarding the R&D process. Insufficient characterization of in vitro activity and/or testing of the hits/leads and insufficient antibacterial activity were the most common critical comments. Other areas of concern were insufficient or lack of differentiation from available drugs or projects with a long R&D history, and the research team's insufficient knowledge of a structured streamlined R&D process as reflected in severe gaps in the expertise of the R&D team. Little appreciation for the problem of the emergence of target-based resistance, especially in single-target approaches, and little awareness of toxicological issues, including approaches with historical liabilities were also commonly mentioned. The shortcomings identified through the analysis of funding applications are echoed by the results of the expert panel.

DISCUSSION

Our analysis identified an urgent need of strengthening the support for antibacterial drug discovery teams to help more projects reach such a quality to be eligible for global funders and private investors.

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

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

Preclinical Characterization and Development on NAQ as a Mu Opioid Receptor Partial Agonist for Opioid Use Disorder Treatment

Mu opioid receptor (MOR) selective antagonists and partial agonists have clinical utility for the treatment of opioid use disorders (OUDs). However, the development of many has suffered due to their poor pharmacokinetic properties and/or rapid metabolism. Our recent efforts to identify MOR modulators have provided 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(isoquinoline-3-carboxamido)morphinan (NAQ), a low-efficacy partial agonist, that showed sub-nanomolar binding affinity to the MOR (K i 0.6 nM) with selectivity over the delta opioid receptor (δ/μ 241) and the kappa opioid receptor (κ/μ 48). Its potent inhibition of the analgesic effect of morphine (AD50 0.46 mg/kg) and precipitation of significantly less withdrawal symptoms even at 100-fold greater dose than naloxone represents a promising molecule for further development as a novel OUD therapeutic agent. Therefore, further in vitro and in vivo characterization of its pharmacokinetics and pharmacodynamics properties was conducted to fully understand its pharmaceutical profile. NAQ showed favorable in vitro ADMET properties and no off-target binding to several classes of GPCRs, enzymes, and ion channels. Following intravenous administration, 1 mg/kg dose of NAQ showed a similar in vivo pharmacokinetic profile to naloxone; however, orally administered 10 mg/kg NAQ demonstrated significantly improved oral bioavailability over both naloxone and naltrexone. Abuse liability assessment of NAQ in rats demonstrated that NAQ functioned as a less potent reinforcer than heroin. Chronic 5 day NAQ pretreatment decreased heroin self-administration in a heroin-vs-food choice procedure similar to the clinically used MOR partial agonist buprenorphine. Taken together, these studies provide evidence supporting NAQ as a promising lead to develop novel OUD therapeutics.

Neuronal differentiation pathways and compound-induced developmental neurotoxicity in the human neural progenitor cell test (hNPT) revealed by RNA-seq

There is an increased awareness that the use of animals for compound-induced developmental neurotoxicity (DNT) testing has limitations. Animal-free innovations, especially the ones based on human stem cell-based models are pivotal in studying DNT since they can mimic processes relevant to human brain development. Here we present the human neural progenitor test (hNPT), a 10-day protocol in which neural progenitor cells differentiate into a neuron-astrocyte co-culture. The study aimed to characterise differentiation over time and to find neurodevelopmental processes sensitive to compound exposure using transcriptomics. 3992 genes regulated in unexposed control cultures (p ≤ 0.001, log2FC ≥ 1) showed Gene Ontology (GO-) term enrichment for neuronal and glial differentiation, neurite extension, synaptogenesis, and synaptic transmission. Exposure to known or suspected DNT compounds (acrylamide, chlorpyrifos, fluoxetine, methyl mercury, or valproic acid) at concentrations resulting in 95% cell viability each regulated unique combinations of GO-terms relating to neural progenitor proliferation, neuronal and glial differentiation, axon development, synaptogenesis, synaptic transmission, and apoptosis. Investigation of the GO-terms 'neuron apoptotic process' and 'axon development' revealed common genes that were responsive across compounds, and might be used as biomarkers for DNT. The GO-term 'synaptic signalling', on the contrary, whilst also responsive to all compounds tested, showed little overlap in gene expression regulation patterns between the conditions. This GO-term may articulate compound-specific effects that may be relevant for revealing differences in mechanism of toxicity. Given its focus on neural progenitor cell to mature multilineage neuronal cell maturation and its detailed molecular readout based on gene expression analysis, hNPT might have added value as a tool for neurodevelopmental toxicity testing in vitro. Further assessment of DNT-specific biomarkers that represent these processes needs further studies.

Functional human cell-based vascularised cardiac tissue model for biomedical research and testing

Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC) are widely used in in vitro biomedical research and testing. However, fully matured, adult cardiomyocyte characteristics have not been achieved. To improve the maturity and physiological relevance of hiPSC-derived cardiomyocytes, we co-cultured them with preconstructed vascular-like networks to form a functional, human cell-based cardiac tissue model. The morphology and gene expression profiles indicated advanced maturation in the cardiac tissue model compared to those of a cardiomyocyte monoculture. The cardiac tissue model’s functionality was confirmed by measuring the effects of 32 compounds with multielectrode array and comparing results to human data. Our model predicted the cardiac effects with a predictive accuracy of 91%, sensitivity of 90% and specificity of 100%. The correlation between the effective concentration (EC50) and the reported clinical plasma concentrations was 0.952 (R² = 0.905). The developed advanced human cell-based cardiac tissue model showed characteristics and functionality of human cardiac tissue enabling accurate transferability of gained in vitro data to human settings. The model is standardized and thus, it would be highly useful in biomedical research and cardiotoxicity testing.

The utility of hERG channel inhibition data in the derivation of occupational exposure limits

Inhibition of the human ether-à-go-go (hERG) channel may lead to QT prolongation and fatal arrhythmia. While pharmaceutical drug candidates that exhibit potent hERG channel inhibition often fail early in development, many drugs with both cardiac and non-cardiac indications proceed to market. In this study, the relationship between in vitro hERG channel inhibition and published occupational exposure limit (OEL) was evaluated. A total of 23 cardiac drugs and 44 drugs with non-cardiac indications with published hERG channel IC₅₀ and published OELs were identified. There was an apparent relationship between hERG IC₅₀ potency and the OEL for cardiac and non-cardiac drugs. Twenty cardiac and non-cardiac drugs were identified that had a potent hERG IC₅₀ (≤25 μM) and a contrastingly large OEL value (≥100 μg/m³). OELs or hazard banding corresponding to ≤100 μg/m³ should be sufficiently protective of effects following occupational exposure to the majority of APIs with hERG IC₅₀ values ≤ 100 μM. It is important to consider hERG IC₅₀ values and possible cardiac effects when deriving OEL values for drugs, regardless of indication. These considerations may be particularly important early in the drug development process for establishing exposure control bands for drugs that do not yet have full clinical safety data.

Direct Contraction Force Measurements of Engineered Cardiac Tissue Constructs With Inotropic Drug Exposure

Contractility is one of the most crucial functions of the heart because it is directly related to the maintenance of blood perfusion throughout the body. Both increase and decrease in contractility may cause fatal consequences. Therefore, drug discovery would benefit greatly from reliable testing of candidate molecule effects on contractility capacity. In this study, we further developed a dual-axis piezoelectric force sensor together with our human cell–based vascularized cardiac tissue constructs for cardiac contraction force measurements. The capability to detect drug-induced inotropic effects was tested with a set of known positive and negative inotropic compounds of isoprenaline, milrinone, omecamtiv mecarbil, propranolol, or verapamil in different concentrations. Both positive and negative inotropic effects were measurable, showing that our cardiac contraction force measurement system including a piezoelectric cantilever sensor and a human cell–based cardiac tissue constructs has the potential to be used for testing of inotropic drug effects.

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.

microRNAs signatures as potential biomarkers of structural cardiotoxicity in human-induced pluripotent stem-cell derived cardiomyocytes

Identification of early biomarkers of heart injury and drug-induced cardiotoxicity is important to eliminate harmful drug candidates early in preclinical development and to prevent severe drug effects. The main objective of this study was to investigate the expression of microRNAs (miRNAs) in human-induced pluripotent stem cell cardiomyocytes (hiPSC-CM) in response to a broad range of cardiotoxic drugs. Next generation sequencing was applied to hiPSC-CM treated for 72 h with 40 drugs falling into the categories of functional (i.e., ion channel blockers), structural (changes in cardiomyocytes structure), and general (causing both functional and structural) cardiotoxicants as well as non-cardiotoxic drugs. The largest changes in miRNAs expression were observed after treatments with structural or general cardiotoxicants. The number of deregulated miRNAs was the highest for idarubicin, mitoxantrone, and bortezomib treatments. RT-qPCR validation confirmed upregulation of several miRNAs across multiple treatments at therapeutically relevant concentrations: hsa-miR-187-3p, hsa-miR-146b-5p, hsa-miR-182-5p (anthracyclines); hsa-miR-365a-5p, hsa-miR-185-3p, hsa-miR-184, hsa-miR-182-5p (kinase inhibitors); hsa-miR-182-5p, hsa-miR-126-3p and hsa-miR-96-5p (common some anthracyclines, kinase inhibitors and bortezomib). Further investigations showed that an upregulation of hsa-miR-187-3p and hsa-miR-182-5p could serve as a potential biomarker of structural cardiotoxicity and/or an additional endpoint to characterize cardiac injury in vitro.

De-risking in Tier I CNS safety assessments is the primary function of study design and technical training of laboratory staff observers

Derisking is not a pharmaceutical industry strategy to reduce time, effort, or costs in drug development. Derisking strategies originated within the National Institutes of Health as a predicate to good science. There is a growing sentiment within drug development programs to diminish the importance of behavioral measures in toxicological studies and in the Tiered Safety assessment plans of the U.S. Regulatory Agencies and the International Commission on Harmonization. The validity and reliability of the Functional Observational Batter (FOB) is critically dependent on consistency and technical quality in each risk assessment plan. US Federal and International drug approval organizations have universally adopted the concept of principles of test construction rather than delineating specific behavioral assay endpoints for inclusion of the FOB in nonclinical safety protocols. The validity and reliability of behavioral observations in standardized neurotoxicity screening is critically dependent on the FOB developed by the Study Director with the Sponsor throughout all stages of testing.. The individual risk factors selected for observation to be included in the early Tier 1 safety program should be determined by the mechanism and mode of action of the test article. The results of Tier I testing are the basis for Tier II testing designs. Critical to the compliance with Good Laboratory Practices is the documentation of training of the operational staff scheduled to conduct all aspects of the established protocol.

Monitoring haemodynamic changes in rodent models to better inform safety pharmacology: Novel insights from

Animal models are essential for assessing cardiovascular responses to novel therapeutics. Cardiovascular safety liabilities represent a leading cause of drug attrition and better preclinical measurements are essential to predict drug-related toxicities. Presently, radiotelemetric approaches recording blood pressure are routinely used in preclinical in vivo haemodynamic assessments, providing valuable information on therapy-associated cardiovascular effects. Nonetheless, this technique is chiefly limited to the monitoring of blood pressure and heart rate alone. Alongside these measurements, Doppler flowmetry can provide additional information on the vasculature by simultaneously measuring changes in blood flow in multiple different regional vascular beds. However, due to the time-consuming and expensive nature of this approach, it is not widely used in the industry. Currently, analysis of waveform data obtained from telemetry and Doppler flowmetry typically examines averages or peak values of waveforms. Subtle changes in the morphology and variability of physiological waveforms have previously been shown to be early markers of toxicity and pathology. Therefore, a detailed analysis of pressure and flowmetry waveforms could enhance the understanding of toxicological mechanisms and the ability to translate these preclinical observations to clinical outcomes. In this review, we give an overview of the different approaches to monitor the effects of drugs on cardiovascular parameters (particularly regional blood flow, heart rate and blood pressure) and suggest that further development of waveform analysis could enhance our understanding of safety pharmacology, providing valuable information without increasing the number of in vivo studies needed.

High-content analysis and Kinetic Image Cytometry identify toxicity and epigenetic effects of HIV antiretrovirals on human iPSC-neurons and primary neural precursor cells

INTRODUCTION

Despite viral suppression due to combination antiretroviral therapy (cART), HIV-associated neurocognitive disorders (HAND) continue to affect half of people with HIV, suggesting that certain antiretrovirals (ARVs) may contribute to HAND.

METHODS

We examined the effects of nucleoside/nucleotide reverse transcriptase inhibitors tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) and the integrase inhibitors dolutegravir (DTG) and elvitegravir (EVG) on viability, structure, and function of glutamatergic neurons (a subtype of CNS neuron involved in cognition) derived from human induced pluripotent stem cells (hiPSC-neurons), and primary human neural precursor cells (hNPCs), which are responsible for neurogenesis.

RESULTS

Using automated digital microscopy and image analysis (high content analysis, HCA), we found that DTG, EVG, and TDF decreased hiPSC-neuron viability, neurites, and synapses after 7 days of treatment. Analysis of hiPSC-neuron calcium activity using Kinetic Image Cytometry (KIC) demonstrated that DTG and EVG also decreased the frequency and magnitude of intracellular calcium transients. Longer ARV exposures and simultaneous exposure to multiple ARVs increased the magnitude of these neurotoxic effects. Using the Microscopic Imaging of Epigenetic Landscapes (MIEL) assay, we found that TDF decreased hNPC viability and changed the distribution of histone modifications that regulate chromatin packing, suggesting that TDF may reduce neuroprogenitor pools important for CNS development and maintenance of cognition in adults.

CONCLUSION

This study establishes human preclinical assays that can screen potential ARVs for CNS toxicity to develop safer cART regimens and HAND therapeutics.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.

In Silico Models for Hepatotoxicity

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

Human Induced Pluripotent Stem Cell as a Disease Modeling and Drug Development Platform-A Cardiac Perspective

A comprehensive understanding of the pathophysiology and cellular responses to drugs in human heart disease is limited by species differences between humans and experimental animals. In addition, isolation of human cardiomyocytes (CMs) is complicated because cells obtained by biopsy do not proliferate to provide sufficient numbers of cells for preclinical studies in vitro. Interestingly, the discovery of human-induced pluripotent stem cell (hiPSC) has opened up the possibility of generating and studying heart disease in a culture dish. The combination of reprogramming and genome editing technologies to generate a broad spectrum of human heart diseases in vitro offers a great opportunity to elucidate gene function and mechanisms. However, to exploit the potential applications of hiPSC-derived-CMs for drug testing and studying adult-onset cardiac disease, a full functional characterization of maturation and metabolic traits is required. In this review, we focus on methods to reprogram somatic cells into hiPSC and the solutions for overcome immaturity of the hiPSC-derived-CMs to mimic the structure and physiological properties of the adult human CMs to accurately model disease and test drug safety. Finally, we discuss how to improve the culture, differentiation, and purification of CMs to obtain sufficient numbers of desired types of hiPSC-derived-CMs for disease modeling and drug development platform.

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.

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

The kidneys, heart and lungs are vital organ systems evaluated as part of acute or chronic toxicity assessments. New methodologies are being developed to predict these adverse effects based on in vitro and in silico approaches. This paper reviews the current state of the art in predicting these organ toxicities. It outlines the biological basis, processes and endpoints for kidney toxicity, pulmonary toxicity, respiratory irritation and sensitization as well as functional and structural cardiac toxicities. The review also covers current experimental approaches, including off-target panels from secondary pharmacology batteries. Current in silico approaches for prediction of these effects and mechanisms are described as well as obstacles to the use of in silico methods. Ultimately, a commonly accepted protocol for performing such assessment would be a valuable resource to expand the use of such approaches across different regulatory and industrial applications. However, a number of factors impede their widespread deployment including a lack of a comprehensive mechanistic understanding, limited in vitro testing approaches and limited in vivo databases suitable for modeling, a limited understanding of how to incorporate absorption, distribution, metabolism, and excretion (ADME) considerations into the overall process, a lack of in silico models designed to predict a safe dose and an accepted framework for organizing the key characteristics of these organ toxicants.

Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants

Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.

An Extensive Metabolomics Workflow to Discover Cardiotoxin-Induced Molecular Perturbations in Microtissues

Discovering modes of action and predictive biomarkers of drug-induced structural cardiotoxicity offers the potential to improve cardiac safety assessment of lead compounds and enhance preclinical to clinical translation during drug development. Cardiac microtissues are a promising, physiologically relevant, in vitro model, each composed of ca. 500 cells. While untargeted metabolomics is capable of generating hypotheses on toxicological modes of action and discovering metabolic biomarkers, applying this technology to low-biomass microtissues in suspension is experimentally challenging. Thus, we first evaluated a filtration-based approach for harvesting microtissues and assessed the sensitivity and reproducibility of nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) measurements of intracellular extracts, revealing samples consisting of 28 pooled microtissues, harvested by filtration, are suitable for profiling the intracellular metabolome and lipidome. Subsequently, an extensive workflow combining nESI-DIMS untargeted metabolomics and lipidomics of intracellular extracts with ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) analysis of spent culture medium, to profile the metabolic footprint and quantify drug exposure concentrations, was implemented. Using the synthetic drug and model cardiotoxin sunitinib, time-resolved metabolic and lipid perturbations in cardiac microtissues were investigated, providing valuable data for generating hypotheses on toxicological modes of action and identifying putative biomarkers such as disruption of purine metabolism and perturbation of polyunsaturated fatty acid levels.

Drug-Induced Seizures: Considerations for Underlying Molecular Mechanisms

A broad spectrum of chemical entities have been associated with drug-induced seizure (DIS), emphasizing the importance of this potential liability across various drug classes (e.g., antidepressants, antipsychotics, antibiotics, and analgesics among others). Despite its importance within drug safety testing, an understanding of the molecular mechanisms associated with DIS is often lacking. The etiology of DIS is understood to be a result of either a deficit in inhibitory (e.g., gamma aminobutyric acid) or an elevated excitatory (e.g., glutamate) signaling, leading to synchronous neuronal depolarization affecting various brain regions and impairing normal neurological functions. How this altered neuronal signaling occurs and how these changes interact with other non-brain receptor driven DIS-associated changes such as metabolic disturbances, electrolyte imbalances, altered drug metabolism, and withdrawal effects are poorly understood. Herein, we discuss important molecular mechanisms identified in DIS for several drugs and/or drug classes. With a better understanding of the molecular mechanisms associated with DIS, in vivo or in vitro models may be applied to characterize and mitigate DIS risk during drug development. Susceptibility stratification for DIS presents species differences in the following order beagle dogs > rodents and cynomolgus monkeys > Göttingen minipigs with a more than 2-fold difference between canines and minipigs, which is important to consider during non-clinical species selection. While clinical signs such as myoclonus, severe muscle jerks, or convulsions are often associated with abnormal epileptiform EEG activity, tremors are most of the time physiological and rarely observed with concurrent epileptiform EEG activity which need to be considered during DIS risk evaluation.

Can preclinical drug development help to predict adverse events in clinical trials?

The development of novel therapeutics is associated with high rates of attrition, with unexpected adverse events being a major cause of failure. Serious adverse events have led to organ failure, cancer development and deaths that were not expected outcomes in clinical trials. These life-threatening events were not identified during therapeutic development due to the lack of preclinical safety tests that faithfully represented human physiology. We highlight the successful application of several novel technologies, including high-throughput screening, organs-on-chips, microbiome-containing drug-testing platforms and humanised mouse models, for mechanistic studies and prediction of toxicity. We propose the incorporation of similar preclinical tests into future drug development to reduce the likelihood of hazardous therapeutics entering later-stage clinical trials.

Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes

Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.